wehub-resource-sync
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: F821
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"""Testcode for Android RPC.
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To use it, start an RPC tracker with "python -m tvm.exec.rpc_tracker".
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Use the tracker's address and port when configuring the RPC app.
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Use "android" as the key if you wish to avoid modifying this script.
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"""
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import os
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import numpy as np
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import tvm
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from tvm import rpc, te
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from tvm.support import ndk, utils
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# Set to be address of tvm proxy.
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tracker_host = os.environ["TVM_TRACKER_HOST"]
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tracker_port = int(os.environ["TVM_TRACKER_PORT"])
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key = "android"
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# Change target configuration.
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# Run `adb shell cat /proc/cpuinfo` to find the arch.
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arch = "arm64"
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target = {"kind": "llvm", "mtriple": f"{arch}-linux-android"}
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# whether enable to execute test on OpenCL target
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test_opencl = False
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# whether enable to execute test on Vulkan target
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test_vulkan = False
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def test_rpc_module():
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# graph
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n = tvm.runtime.convert(1024)
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A = te.placeholder((n,), name="A")
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B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
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a_np = np.random.uniform(size=1024).astype(A.dtype)
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temp = utils.tempdir()
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# Establish remote connection with target hardware
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tracker = rpc.connect_tracker(tracker_host, tracker_port)
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remote = tracker.request(key, priority=0, session_timeout=60)
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mod = tvm.IRModule.from_expr(te.create_prim_func([A, B]).with_attr("global_symbol", "myadd"))
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sch = tvm.s_tir.Schedule(mod)
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(x,) = sch.get_loops(block=sch.get_sblock("B"))
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xo, xi = sch.split(i, [None, 32])
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sch.bind(xo, "blockIdx.x")
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sch.bind(xi, "threadIdx.x")
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if test_opencl:
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f = tvm.compile(sch.mod, target=tvm.target.Target("opencl", host=target))
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path_dso_cl = temp.relpath("dev_lib_cl.so")
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f.export_library(path_dso_cl, fcompile=ndk.create_shared)
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print("Run GPU(OpenCL Flavor) test ...")
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dev = remote.cl(0)
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remote.upload(path_dso_cl)
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f1 = remote.load_module("dev_lib_cl.so")
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a = tvm.runtime.tensor(a_np, dev)
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b = tvm.runtime.tensor(np.zeros(1024, dtype=A.dtype), dev)
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time_f = f1.time_evaluator(f1.entry_name, dev, number=10)
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cost = time_f(a, b).mean
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print(f"{cost:g} secs/op\n")
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np.testing.assert_equal(b.numpy(), a.numpy() + 1)
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if __name__ == "__main__":
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test_rpc_module()
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